Applied evolution: An integrated approach to studying life history traits in response to drug selection

The use of chemical control measures to reduce the impact of parasite and pest species has frequently resulted in the development of resistance. Thus, resistance management has become a key concern in human and veterinary medicine, and in agricultural production. Although it is known that factors such as gene flow between susceptible and resistant populations, drug type, application methods, and costs of resistance can affect the rate of resistance evolution, less is known about the impacts of density-dependent eco-evolutionary processes that could be altered by drug-induced mortality. The overall aim of this thesis was to take an experimental evolution approach to assess how life history traits respond to drug selection, using a free-living dioecious worm (Caenorhabditis remanei) as a model. In Chapter 2, I defined the relationship between C. remanei survival and Ivermectin dose over a range of concentrations, in order to control the intensity of selection used in the selection experiment described in Chapter 4. The dose-response data were also used to appraise curve-fitting methods, using Akaike Information Criterion (AIC) model selection to compare a series of nonlinear models. The type of model fitted to the dose response data had a significant effect on the estimates of LD50 and LD99, suggesting that failure to fit an appropriate model could give misleading estimates of resistance status. In addition, simulated data were used to establish that a potential cost of resistance could be predicted by comparing survival at the upper asymptote of dose-response curves for resistant and susceptible populations, even when differences were as low as 4%. This approach to dose-response modeling ensures that the maximum amount of useful information relating to resistance is gathered in one study. In Chapter 3, I asked how simulations could be used to inform important design choices used in selection experiments. Specifically, I focused on the effects of both within- and between-line variation on estimated power, when detecting small, medium and large effect sizes. Using mixed-effect models on simulated data, I demonstrated that commonly used designs with realistic levels of variation could be underpowered for substantial effect sizes. Thus, use of simulation-based power analysis provides an effective way to avoid under or overpowering a study designs incorporating variation due to random effects. In Chapter 4, I 3 investigated how Ivermectin dosage and changes in population density affect the rate of resistance evolution. I exposed replicate lines of C. remanei to two doses of Ivermectin (high and low) to assess relative survival of lines selected in drug-treated environments compared to untreated controls over 10 generations. Additionally, I maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug treatment versus ecological processes affected by changes in density-dependent feedback. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. The results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures, which could result in misleading conclusions about the evolution of heritable resistance following drug treatment. In Chapter 5, I investigated whether the apparent changes in drug susceptibility found in Chapter 4 were related to evolved changes in life-history of C. remanei populations after selection in drug-treated and random-mortality environments. Rapid passage of lines in the drug-free environment had no effect on the measured life-history traits. In the drug-free environment, adult size and fecundity of drug-selected lines increased compared to the controls but drug selection did not affect lifespan. In the treated environment, drug-selected lines showed increased lifespan and fecundity relative to controls. Adult size of randomly culled lines responded in a similar way to drug-selected lines in the drug-free environment, but no change in fecundity or lifespan was observed in either environment. The results suggest that life histories of nematodes can respond to selection as a result of the application of control measures. Failure to take these responses into account when applying control measures could result in adverse outcomes, such as larger and more fecund parasites, as well as over-estimation of the development of genetically controlled resistance. In conclusion, my thesis shows that there may be a complex relationship between drug selection, density-dependent regulatory processes and life history of populations challenged with control measures. This relationship could have implications for how resistance is monitored and managed if life histories of parasitic species show such eco-evolutionary responses to drug application.

MicroRNA modulation of aldosterone production in the adrenal gland

Hypertension is the major risk factor for coronary disease worldwide. Primary hypertension is idiopathic in origin but is thought to arise from multiple risk factors including genetic, lifestyle and environmental influences. Secondary hypertension has a more definite aetiology; its major single cause is primary aldosteronism (PA), the greatest proportion of which is caused by aldosteroneproducing adenoma (APA), where aldosterone is synthesized at high levels by an adenoma of the adrenal gland. There is strong evidence to show that high aldosterone levels cause adverse effects on cardiovascular, cerebrovascular, renal and other systems. Extensive studies have been conducted to analyse the role that regulation of CYP11B2, the gene encoding the aldosterone synthase enzyme plays in determining aldosterone production and the development of hypertension. One significant regulatory factor that has only recently emerged is microRNA (miRNA). miRNAs are small non-coding RNAs, synthesized by a series of enzymatic processes, that negatively regulate gene expression at the posttranscriptional level. Detection and manipulation of miRNA is now known to be a viable method in the treatment, prevention and prognosis of certain diseases. The aim of the present study was to identify miRNAs likely to have a role in the regulation of corticosteroid biosynthesis. To achieve this, the miRNA profile of APA and normal human adrenal tissue was compared, as was the H295R adrenocortical cell line model of adrenocortical function, under both basal conditions and following stimulation of aldosterone production. Key differentially-expressed miRNAs were then identified and bioinformatic tools used to identify likely mRNA targets and pathways for these miRNAs, several of which were investigated and validated using in vitro methods. The background to this study is set out in Chapter 1 of this thesis, followed by a description of the major technical methods employed in Chapter 2. Chapter 3 presents the first of the study results, analysing differences in miRNA profile between APA and normal human adrenal tissue. Microarray was implemented to detect the expression of miRNAs in these two tissue types and several miRNAs were found to vary significantly and consistently between them. Furthermore, members of several miRNA clusters exhibited similar changes in expression pattern between the two tissues e.g. members of cluster miR-29b-1 (miR-29a-3p and miR-29b-3p) and of cluster miR-29b-2 (miR-29b-3p and miR-29c- 3p) are downregulated in APA, while members of cluster let-7a-1 (let-7a-5p and let-7d-5p), cluster let-7a-3 (let-7a-5p and let-7b-5p) and cluster miR-134 (miR- 134 and miR-382) are upregulated. Further bioinformatic analysis explored the possible biological function of these miRNAs using Ingenuity® Systems Pathway Analysis software. This led to the identification of validated mRNAs already known to be targeted by these miRNAs, as well as the prediction of other mRNAs that are likely targets and which are involved in processes relevant to APA pathology including cholesterol synthesis (HMGCR) and corticosteroidogenesis (CYP11B2). It was therefore hypothesised that increases in miR-125a-5p or miR- 335-5p would reduce HMGCR and CYP11B2 expression. Chapter 4 describes the characterisation of H295R cells of different strains and sources (H295R Strain 1, 2, 3 and HAC 15). Expression of CYP11B2 was assessed following application of 3 different stimulants: Angio II, dbcAMP and KCl. The most responsive strain to stimulation was Strain 1 at lower passage numbers. Furthermore, H295R proliferation increased following Angio II stimulation. In Chapter 5, the hypothesis that increases in miR-125a-5p or miR-335-5p reduces HMGCR and CYP11B2 expression was tested using realtime quantitative RT-PCR and transfection of miRNA mimics and inhibitors into the H295R cell line model of adrenocortical function. In this way, miR-125a-5p and miR-335-5p were shown to downregulate CYP11B2 and HMGCR expression, thereby validating certain of the bioinformatic predictions generated in Chapter 3. The study of miRNA profile in the H295R cell lines was conducted in Chapter 6, analysing how it changes under conditions that increase aldosterone secretion, including stimulation Angiotensin II, potassium chloride or dibutyryl cAMP (as a substitute for adrenocorticotropic hormone). miRNA profiling identified 7 miRNAs that are consistently downregulated by all three stimuli relative to basal cells: miR-106a-5p, miR-154-3p, miR-17-5p, miR-196b-5p, miR-19a-3p, miR-20b- 5p and miR-766-3p. These miRNAs include those derived from cluster miR-106a- 5p/miR-20b-5p and cluster miR-17-5p/miR-19a-3p, each producing a single polycistronic transcript. IPA bioinformatic analysis was again applied to identify experimentally validated and predicted mRNA targets of these miRNAs and the key biological pathways likely to be affected. This predicted several interactions between miRNAs derived from cluster miR-17-5p/miR-19a-3p and important mRNAs involved in cholesterol biosynthesis: LDLR and ABCA1. These predictions were investigated by in vitro experiment. miR-17-5p/miR-106a-p and miR-20b-5p were found to be consistently downregulated by stimulation of aldosterone biosynthesis. Moreover, miR-766-3p was upregulation throughout. Furthermore, I was able to validate the downregulation of LDLR by miR-17 transfection, as predicted by IPA. In summary, this study identified key miRNAs that are differentially-expressed in vivo in cases of APA or in vitro following stimulation of aldosterone biosynthesis. The many possible biological actions these miRNAs could have were filtered by bioinformatic analysis and selected interactions validated in vitro. While direct actions of these miRNAs on steroidogenic enzymes were identified, cholesterol handling also emerged as an important target and may represent a useful point of intervention in future therapies designed to modulate aldosterone biosynthesis and reduce its harmful effects.